The Study Of Differentially Expressed Gene Dtetction Based On R Language

Bioinformatics in the 21st century, the era of information society and life sciences, has become an emerging discipline. Bioinformatics is a product of interdisciplinary, including biology, computer science, mathematics, physics, information science, and many other fields. Having Internet as the media and databases as the carrier, Bioinformatics applies a variety of mathematical theory to build computer models, and contributes to the processing, storage, retrieval, analysis and interpretation of the biological meaning contained in the mass biological data from biological experiments. This paper discussed the study and use of bioinformatics tools, i.e. R language and Bioconductor bioinformatics software set, to address the problem of bioinformatics, and through the T statistics discussed the application of R language in differentially expressed genes detection. R language as a tool for graphical display and statistical calculations is a common computer language based on open software platform and has good portability and flexibility. In the field of biology, especially for structural and functional genomics data analysis, Bioconductor software is a set of very typical applications.In this paper, R language and Bioconductor bioinformatics software set in bioinformatics was discussed, and the main study commenced as follows:1) In-depth understanding of the definition of bioinformatics, as well as characteristics and significance, the main research content of microarray gene expression data analysis approach, the application of bioinformatics databases.2) Summary of the characteristics, basic principles and core of R language. R language is an important bioinformatics software platform for large-scale biological data analysis for its own open-source, easy to handle and many other features.3) Bioconductor bioinformatics software collection is a open scientific computing software platform based on the R language environment for biological information management and analysis,, including large number of useful bio-chip packages such as data analysis and genomic data chip, etc., Bioconductor open source software are helpful as both learning materials and tools for sample data analysis.4) Summarizes the detection of differentially expressed genes in the pretreatment process, and makes improvement based on in T-statistics method.5) Introduced the ROC curve to evaluate T-statistics, improved modified T-statistics and Half T-statistics.In summary, this paper focused on the application of R language in bioinformatics, summarized and analyzed the contents of bioinformatics as well as learning R language knowledge. For the detection of differentially expressed genes, we improved and proposed a method based on the T-statistics, and gave a thorough evaluation by comparing to other methods.